Learning to Represent Bilingual Dictionaries

Bilingual word embeddings have been widely used to capture the correspondence of lexical semantics in different human languages. However, the cross-lingual correspondence between sentences and lexicons is less studied, despite that this correspondence can largely benefit many applications, such as cross-lingual semantic search and question answering. To bridge this gap, we propose a neural embedding model that leverages bilingual dictionaries. The proposed model is trained to map the literal word definitions to the cross-lingual target words, for which we explore with different sentence encoding techniques. To enhance the learning process on limited resources, our model adopts several critical learning strategies, including multi-task learning on different bridges of languages, and joint learning of the dictionary model with a bilingual word embedding model. We conduct experiments on two tasks: (i) cross-lingual reverse dictionary retrieval, and (ii) bilingual paraphrase identification. In the former task, we demonstrate that our model is capable of comprehending bilingual concepts based on descriptions, and we also highlight the effectiveness of proposed learning strategies. In the latter one, we show that the proposed model effectively associates sentences in different languages via a shared embedding space, and outperforms existing approaches in identifying bilingual paraphrases.